Key Takeways
- Bittensor runs a decentralized network where AI models earn TAO tokens by contributing useful outputs, validated and ranked by other participants across various subnets.
- It uses a blockchain built on Substrate to manage rewards, track performance, and support a fixed-supply token economy without any initial private allocations.
- The dTAO system links subnet rewards to market demand through Alpha tokens, shifting control from validators to token holders, though early misuse exposed weaknesses.
- Bittensor supports applications like chatbots and content moderation, with ongoing development driven by open community governance and real-world machine learning contributions.
In April 2024, OpenAI announced a partnership with Stack Overflow to train future large language models using publicly available developer discussions. Meanwhile, a less mainstream project had already created a system where contributions to artificial intelligence (AI) could be measured, evaluated, and rewarded by a network of peers rather than governed by tech giants or platform policies.
The idea? Turn machine learning into a public good—accessible, decentralized, and owned by the participants who build it.
That’s the premise of Bittensor, a project that has quietly drawn in hundreds of developers, data scientists, and crypto natives since it launched in 2021. But what is Bittensor, exactly? And how does TAO and dTAO fit into the picture?
Let’s take it apart, one layer at a time.
What is Bittensor?
Bittensor is a decentralized network designed to support and reward machine learning. Rather than concentrating AI development in a handful of tech companies, Bittensor spreads that work across thousands of crypto nodes run by independent participants. Every model on the network competes to answer questions better and faster than others—and gets rewarded when it does.
The incentive mechanism is at the heart of this system. Participants contribute compute power, build better models, or create new tasks while earning TAO, the network’s native token. The better their model performs, the more TAO they earn.
It sounds like a simple concept, but Bittensor is a lot more layered under the hood.
The network launched in 2021, co-founded by developers Ala Shaabana and Jacob Robert Steeves. Their idea was to create an open marketplace for intelligence, one where value accrues to the best contributors rather than centralized platforms.
Over time, this marketplace grew into a full-fledged protocol with hundreds of active nodes and multiple subnets, each focused on a different kind of AI task. Some handle translation. Others focus on image generation, reasoning, or data curation. And instead of one large neural network, Bittensor acts as an economy where each model, or “miner,” competes for reputation and rewards.
How does Bittensor work? Key components
At the center of it all sits the Bittensor blockchain. Surrounding it are subnets, participants, and consensus mechanisms that help it scale beyond what a single AI project could do.
Let’s start with subnets.
Bittensor Subnets
In Bittensor, subnets are specialized mini-networks where models perform a particular task. One subnet might be built around summarizing text, while another could focus on audio classification. Each subnet is essentially a small ecosystem that evaluates and ranks model performance based on the usefulness of its responses.
Here’s how it works.
Let’s say a subnet is created for question-answering. Dozens or even hundreds of models join this subnet. When a user sends a prompt, the models compete to answer it. Validators, nodes responsible for scoring, rate the answers, and the best-performing models earn TAO.
This model creates two feedback loops: one for technical performance (did the model answer well?) and one for economic value (how many tokens did it earn relative to peers?). Because each subnet runs independently, they can scale horizontally, allowing the entire network to grow without hitting bottlenecks.
Bittensor Blockchain
The Bittensor blockchain operates on a unique Proof-of-Stake mechanism, often referred to as Yuma Consensus or “Proof of Intelligence.” This consensus model secures the network and manages how TAO tokens are distributed. Like many blockchains, it timestamps transactions and provides a transparent history of contributions and rewards.
However, unlike traditional blockchains, Bittensor also tracks neuron scores—a measure of how well each AI model performs within its subnet. These scores are critical as they directly influence the TAO and subnet “alpha token” inflation mechanism. The more useful and high-performing a miner’s AI model is to the network, the greater share of the emitted tokens it will receive. This creates a competitive market for machine intelligence.
Network participants
Bittensor wouldn’t function without a range of specialized participants, each playing a different role in maintaining the ecosystem’s balance.
Miners are models who provide answers to subnet prompts. Others in the subnet evaluate their performance, and if they rank well, they earn TAO.
Validators evaluate responses submitted by miners. They score quality and assign “weights” that influence miners’ reward.
Subnet Creators propose and launch new AI subnets. These individuals decide what task the subnet will perform, then configure its parameters and incentives. Once live, the subnet runs independently.
Stakers contribute TAO to back miners or validators they believe in. If the backed participant performs well, both the staker and the participant earn rewards. It’s a bit like voting with your money on who you think will perform best.
What is TAO Token?
TAO is the native token of the Bittensor network. It serves three main purposes:
- It rewards useful AI contributions.
- It governs who has influence in the network.
- It acts as a signal for stakers and builders.
According to Bittensor’s halving website, Bittensor’s native token, TAO, has a maximum supply capped at 21 million tokens. The network distributes new tokens through a competitive emission mechanism, beginning with 1 TAO per block. Blocks are produced approximately every 12 seconds, which results in about 7,200 TAO per day. The emission rate is reduced over time through a halving schedule that occurs based on the total amount of TAO emitted, not on a fixed calendar interval.
In the network, TAO also plays a signaling role. The more TAO staked on a miner or validator, the more influence they have in that subnet’s economy. This staked weight helps determine how inflation rewards are split, giving stakers and builders skin in the game.
It’s also worth noting that TAO is used as collateral to register new subnets. This requirement helps filter out low-quality subnet proposals, since launching one requires a financial commitment.
What is dTAO Token?
In 2024, a fork of Bittensor emerged called dTAO—short for Dynamic TAO. The fork addressed concerns about Bittensor’s growing centralization, particularly around validator influence and subnet governance.
The dTAO project introduces a reworked version of the network that includes:
- Hard limits on validator control,
- More democratic subnet creation processes,
- And revised inflation mechanics that redistribute rewards in a flatter curve.
Unlike TAO, dTAO has no fixed cap on supply. It’s governed by a different incentive model designed to support subnet innovation better and attract early-stage contributors.
Some in the community see dTAO as a way to revive Bittensor’s original spirit—one where small contributors can still make an impact. Others view it as unnecessary fragmentation. Either way, dTAO has quickly become part of the broader conversation around decentralized machine learning.
Bittensor use cases
So what’s actually being built with Bittensor?
Plenty, it turns out. Each subnet can support a different use case, and developers have already begun experimenting across various tasks.
Here are a few examples:
- Language translation: Subnets that handle real-time translation between dozens of languages.
- Text summarization: Models that condense long articles or documents into key takeaways.
- Content filtering: Tools that scan for toxicity or hate speech in social media posts.
- Data labeling: Automated systems that help tag and classify datasets for training.
- Prompt ranking: Subnets where models learn to evaluate the quality of user-submitted prompts.
Some builders are using Bittensor to test early-stage ideas before launching them as standalone products. Others rely on the network’s competitive incentives to fine-tune models without paying cloud providers.
Governance and Community
Unlike traditional projects run by a centralized foundation or core team, Bittensor’s future depends on its participants. TAO holders can propose and vote on protocol upgrades. Subnet creators set the tone for their ecosystems. Validators influence which miners rise or fall in reputation.
And although governance remains off-chain for now, discussions around formalizing it have gained momentum, especially with the introduction of projects like dTAO.
The community, spread across Discord servers, GitHub repositories, and Telegram groups, plays a major role in driving development. Weekly subnet meetings, validator discussions, and governance brainstorming sessions are common. It’s not a monolith, but a loose collection of contributors aligned by a shared interest in decentralized intelligence.
Closing Thoughts
Bittensor offers a compelling alternative to the way AI is typically developed and monetized. Instead of building a single model and charging access fees, it creates a live economy where machine learning models are evaluated, ranked, and rewarded for their usefulness.
TAO, and now dTAO, make that possible. Together, they give participants a way to earn, govern, and build—without relying on centralized companies or APIs.
Whether Bittensor will grow into a critical layer of the future AI ecosystem or remain a niche experiment is still an open question. But for now, it’s offering a glimpse at what decentralized machine learning might actually look like.
